Stress Detection Based on Multi-class Probabilistic Support Vector Machines for Accented English Speech
Computer Science and Information Engineering, World Congress on (2009)
Los Angeles, California USA
Mar. 31, 2009 to Apr. 2, 2009
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CSIE.2009.739
A stress detection based on multi-class probabilistic support vector machines (MCP-SVMs) is proposed for classifying speech into following categories - no stress, primary stress, and secondary stress. The stress classifier is performed with a feature set including perceptual features, MFCC, delta-MFCC and delta-delta-MFCC. To observe that speakers from the same accent regions had similar tendencies in mispronunciations including word stress, this work uses English Across Taiwan (EAT) to represent Taiwanese-accented English speech corpora. The overall performance in the experimental results achieves about 84% classification of accuracy.
stress detection, English Across Taiwan, multi-class probabilistic support vector machines
Jhing-Fa Wang, Jia-Ching Wang, Gung-Ming Chang, Shun-Chieh Lin, "Stress Detection Based on Multi-class Probabilistic Support Vector Machines for Accented English Speech", Computer Science and Information Engineering, World Congress on, vol. 07, no. , pp. 346-350, 2009, doi:10.1109/CSIE.2009.739